(2024) 21:47
Yamada et al. Fluids and Barriers of the CNS
https://doi.org/10.1186/s12987-024-00552-6

Fluids and Barriers of the CNS

Open Access

RESEARCH

Modeling cerebrospinal fluid dynamics
across the entire intracranial space
through integration of four‑dimensional flow
and intravoxel incoherent motion magnetic
resonance imaging
Shigeki Yamada1,2* , Tomohiro Otani3, Satoshi Ii4,5, Hirotaka Ito6, Chifumi Iseki7,8, Motoki Tanikawa1,
Yoshiyuki Watanabe9, Shigeo Wada3, Marie Oshima2 and Mitsuhito Mase1

Abstract
Background Bidirectional reciprocal motion of cerebrospinal fluid (CSF) was quantified using four-dimensional(4D) flow magnetic resonance imaging (MRI) and intravoxel incoherent motion (IVIM) MRI. To estimate various CSF
motions in the entire intracranial region, we attempted to integrate the flow parameters calculated using the two MRI
sequences. To elucidate how CSF dynamics deteriorate in Hakim’s disease, an age-dependent chronic hydrocephalus,
flow parameters were estimated from the two MRI sequences to assess CSF motion in the entire intracranial region.
Methods This study included 127 healthy volunteers aged ≥ 20 years and 44 patients with Hakim’s disease. On 4D
flow MRI for measuring CSF motion, velocity encoding was set at 5 cm/s. For the IVIM MRI analysis, the diffusionweighted sequence was set at six b-values (i.e., 0, 50, 100, 250, 500, and 1000 s/mm2), and the biexponential IVIM
fitting method was adapted. The relationships between the fraction of incoherent perfusion (f) on IVIM MRI and 4D
flow MRI parameters including velocity amplitude (VA), absolute maximum velocity, stroke volume, net flow volume,and reverse flow rate were comprehensively evaluated in seven locations in the ventricles and subarachnoid spaces.
Furthermore, we developed a new parameter for fluid oscillation, the Fluid Oscillation Index (FOI), by integrating these
two measurements. In addition, we investigated the relationship between the measurements and indices specificto Hakim’s disease and the FOIs in the entire intracranial space.
Results The VA on 4D flow MRI was significantly associated with the mean f-values on IVIM MRI. Therefore, we estimated VA that could not be directly measured on 4D flow MRI from the mean f-values on IVIM MRI in the intracranial
CSF space, using the following formula; e0.2(f−85) + 0.25. To quantify fluid oscillation using one integrated parameterwith weighting, FOI was calculated as VA × 10 + f × 0.02. In addition, the FOIs at the left foramen of Luschka had
the strongest correlations with the Evans index (Pearson’s correlation coefficient: 0.78). The other indices relatedwith Hakim’s disease were significantly associated with the FOIs at the cerebral aqueduct and bilateral foramina
of Luschka. FOI at the cerebral aqueduct was also elevated in healthy controls aged ≥ 60 years.

*Correspondence:
Shigeki Yamada
shigekiyamada393@gmail.com
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Yamada et al. Fluids and Barriers of the CNS(2024) 21:47

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Conclusions We estimated pulsatile CSF movements in the entire intracranial CSF space in healthy individuals
and patients with Hakim’s disease using FOI integrating VA from 4D flow MRI and f-values from IVIM MRI. FOI is usefulfor quantifying the CSF oscillation.
Keywords Cerebrospinal fluid dynamics, Idiopathic normal pressure hydrocephalus, Aging, Hakim’s disease,
Idiopathic normal pressure hydrocephalus, Aging, CSF motion, Fluid oscillation, 4D Flow MRI, Intravoxel incoherent
motion, Diffusion-weighted image

BackgroundThe dynamics of cerebrospinal fluid (CSF) have been
demonstrated in many imaging studies and animal models, but remains mysterious. Complex CSF motions comprise a steady microflow produced by the rhythmic wavy
movement of motile cilia on the ventricular wall surface[1, 2]; dynamic multidirectional pulsatile, laminar, andturbulent flows produced by pulsations of the brain andcerebral arteries [2–8], and an uncertain flow produced
by respiration and head movement [9–12]. However,measuring very small complex CSF motions with a velocity of < 0.1 cm/s had been difficult using conventional
phase-contrast magnetic resonance imaging (MRI) [4,13] and four-dimensional (4D) flow MRI [6, 7, 14–16].
Furthermore, previous CSF dynamic studies have measured pulsatile CSF motions in the ventricles, including
the foramen of Magendie, cerebral aqueduct, and foramina of Monro, and subarachnoid spaces in the posterior
fossa, including the prepontine cistern and craniocervicaljunction, but not in the Sylvian fissures or the convexity
part of the subarachnoid spaces [3–8, 13–16]. Furthermore, these complex pulsatile CSF motions have been
considered to be mainly driven by arterial pulsations [10,15, 17, 18] and decrease with aging because of declines inbrain volume, arterial elasticity, and circulating cerebral
blood volume [6, 8, 19, 20]. These age-related changes in
CSF dynamics and volumes, including metabolism, are ofinterest because they are thought to be associated withdementia and neurodegenerative diseases by impairing
the excretion of neurotoxic wastes from the brain [18,
21–23]. Moreover, tightened sulci in high convexity [24–26] have been recognized as the most important imaging
finding for Hakim’s disease, which has been called idiopathic normal-pressure hydrocephalus [27] in contrast
to brain atrophy in the elderly population. Accumulating evidence supports the hypothesis that various alterations in CSF dynamics contribute to disproportionately
enlarged subarachnoid spaces and ventricles in Hakim’s
disease, which is considered an age-dependent chronichydrocephalus. We previously reported the usefulnessof the mean value of fraction of incoherent perfusion (f)on intravoxel incoherent motion (IVIM) MRI for evaluating small complex CSF motions in the entire intracranial
CSF space, comparing brains with Hakim’s disease withhealthy aging brains [28], although their direction andflow velocity could not be examined on IVIM MRI [29–31]. Le Bihan et al. described visualization of CSF motionin their first paper on IVIM MRI [29], and the possibility
that f values could quantify CSF motion in their second
paper [30]. Therefore, we hypothesized that complex CSF
motions in the entire intracranial space could be simulated using flow velocity parameters on 4D flow MRI andf on IVIM MRI. Confirming this hypothesis may contribute to the elucidation of CSF dynamics in Hakim’s disease
and aging effects. Therefore, this study aimed to estimate
CSF motion from dynamic pulsatile flow to fine microflow by integrating f on IVIM MRI and flow velocity
parameters on 4D flow MRI. In addition, our second purpose is to elucidate how alterations in CSF dynamics contribute to enlarged subarachnoid spaces and ventricles in
Hakim’s disease, using high-resolution 3D morphologicalmodels of the ventricles and subarachnoid spaces in theentire intracranial space. Furthermore, this study compared the distribution of CSF motions in healthy aging
volunteers with that in patients with Hakim’s disease.

Methods
Study population

In total, 128 healthy volunteers aged ≥ 20 years who
underwent both 4D flow MRI and IVIM MRI on a 3-T
brain MRI scanner (Discovery MR 750W, GE Medical
Systems, Inc. Chicago, IL, USA) from November 2020
to February 2022 were recruited from the medical staff,
students, and their family members by open recruitment.
The inclusion criteria for the volunteers were as follows:
individuals who had no history of brain injury, brain
tumor, or cerebrovascular disease on previous brain MRI
and those who had never undergone brain MRI and had
no neurological symptoms, including cognitive function. One volunteer aged 84 years was excluded from thisstudy because he had a history of head surgery due to ahead injury over 30 years ago. Patients’ MRI data wereused in an opt-out method, after their personal information was anonymized in a linkable manner. Furthermore,
25 patients diagnosed with Hakim’s disease who underwent MRI using the same GE MRI machine as that in thevolunteers until 2022 and 19 patients with Hakim’s disease who underwent MRI using a Philips MRI machineYamada et al. Fluids and Barriers of the CNS(2024) 21:47(Ingenia Elition 3.0 T Philips, Amsterdam, Netherlands)
since 2023 were included in this study. All patients with
Hakim’s disease had radiological findings of disproportionately enlarged subarachnoid space hydrocephalus (DESH) [32–34], specifically ventricular dilatation,
enlarged Sylvian fissure, and narrow sulci at high convexity [26], and triad symptoms of gait disturbance, cognitive
impairment, and urinary incontinence, according to thethird edition of the Japanese guidelines for the management of idiopathic normal-pressure hydrocephalus [35].
Of them, 26 patients (59%) underwent CSF removal (30–35 mL) via a lumbar tap and were evaluated for changes
in their symptoms before and 1 and 2 days after the CSF
tap test. Furthermore, 25 patients (57%) underwent CSF
shunt surgery, and their symptoms improved by ≥ 1 pointon the modified Rankin Scale and/or the Japanese grading scale [35]. Other patients were not recommended toundergo an aggressive tap test or shunt surgery because
their symptoms were very mild or they could not walk,and no improvement in symptoms was observed after thetap test or shunt surgery.
Image acquisition

The sequence parameters for 4D flow MRI using a GE
MRI machine were as follows: repetition time, variable(10–20 ms); echo time, variable (3–7 ms); flip angle, 8°;
field of view, 200 mm; matrix size, 256 × 256; voxel size,
0.781 × 0.781 × 1.0 mm; number of cardiac phases, 12;
and velocity encoding, 5 cm/s. The 4D flow sequenceparameters for a Philips MRI machine were as follows:
repetition time, variable (10–20 ms); echo time, variable (3–7 ms); flip angle, 8°; field of view, 200 mm; matrix
size, 208 × 198; voxel size, 0.89 × 0.89 × 1.0 mm; numberof cardiac phases, 8; and velocity encoding, 5 cm/s. The
image range was in the mid-sagittal plane with a width of
30 mm (1.0 mm × 30 slices), encompassing areas from thebilateral foramina of Monro to the upper cervical subarachnoid spaces. The imaging time was approximately
10 min, depending on the individual heart rate, which
was synchronized with the peripheral pulse rate measured from the finger. Because the original T1-weighted
magnitude images, including the 4D flow sequence set,
did not have sufficient resolution, the 3D T2-weighted
fast spin-echo sequence was alternatively performed toobtain additional anatomical information, with the following parameters: repetition time, 2000 ms; echo time,85.3 ms; matrix 288 × 288; voxel size, 0.8 × 0.8 × 0.8 mm;
and acquisition time, approximately 4 min.
For the IVIM analysis, diffusion-weighted imaging
was performed in the axial plane using six b values (i.e.,
0, 50, 100, 250, 500, and 1000 s/mm2). The sequenceparameters of a GE MRI machine were as follows: repetition time, variable (6000–7000 ms); echo time, variable

Page 3 of 15(75–80 ms); flip angle, 90°; slice thickness, 3.0 mm; fieldof view, 220 mm; acquisition matrix size, 128 × 192; pixel
size, 1.7 × 1.1 mm; acquisition time, approximately 2 minfor total acquisition; motion-probing gradient, bipolar
type. The IVIM sequence parameters for a Philips MRI
machine were as follows: repetition time, variable (10–20 ms); echo time, variable (3–7 ms); flip angle, 90°; slice
thickness, 3.0 mm; field of view, 220 mm; acquisition
matrix size, 123 × 192; pixel size, 0.98 × 0.98 mm. The isotropic images were created from three orthogonal diffuse
gradient pulse images.
4D flow MRI analysis

Details of the acquisition method for 4D flow MRI were
described in our previous papers [6, 7, 20]. In summary,in the first step of 4D flow MRI, 3D velocity encoding
data obtained from triaxial phase-contrast images of 4D
flow MRI and morphological data of the intracranial CSF
space obtained from 3D T2-weighted MRI were combined using a commercialized 4D flow application on an
independent 3D volume analyzer workstation (SYNAPSE
3D; FUJIFILM Corporation, Tokyo, Japan), as shown in
Fig. 1. In the second step, the area to be analyzed by 4D
flow MRI was extracted. In the third step, automatedpolynomial fitting properly was used to correct for phaseoffsets, eddy currents and background noise, enabling
accurate measurements of slow CSF flow velocities [36].
Finally, the 3D flow velocity vectors, including the average, maximum, and minimum values of triaxial velocities(cm/s), stroke volume (μL/heartbeat), net flow volume(μL/heartbeat), and reverse flow rate (%), were measured
at nine regions of interest (ROIs) based on anatomical
features: ventral and dorsal aspects of the foramen magnum, foramen of Magendie, lower and upper parts of thecerebral aqueduct, bilateral foramina of Monro, premedullary cistern, and prepontine cistern on the orthogonal planes perpendicular to the mid-sagittal section andperpendicular to the main axis of CSF motion. Because
the CSF moves bidirectionally, velocity amplitude (VA)
(cm/s) was defined as the difference between the maximum velocity and the minimum velocity (typically the
maximum velocity in the opposite direction) at each ROI.
Intravoxel incoherent motion analysis

IVIM analysis to quantitatively evaluate small pulsatile complex CSF motions was performed as previously reported [28]. In brief, from the six low b-valuesof DWI images (0, 50, 100, 250, 500, and 1000 s/mm2),
the commercialized IVIM application on the SYNAPSE
3D workstation creates f maps, concurrently with otherparameters, including apparent diffusion coefficient(ADC, ­mm2/s), true diffusion coefficient (D, m­ m2/s), andYamada et al. Fluids and Barriers of the CNS(2024) 21:47

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Fig. 1 Schema of our methods of 4D flow MRI and intravoxel incoherent motion (IVIM) MRI. The figures on the left illustrate (A–D) the 4D Flow
MRI, while those on the right (E–G) depict the IVIM MRI, both acquired from a single healthy volunteer aged 85 years. A Demonstrates the initiationof the 4D Flow app. Four-dimensional analysis is performed utilizing the 3D T2-weighted MRI (leftmost), the morphological image of the 4D
flow MRI (second from the left) as the reference, and tri-axis phase images (right three). Following the extraction of the area designated for 4D
flow analysis (B, light green), background correction is executed (C), which is fully automated with a single click. Finally, an observation phase
is undertaken to evaluate CSF flow velocity (D). In E, the IVIM app has just been launched, and all six low b-values of DWI images (0, 50, 100, 250,
500, and 1000 s/mm2) are integrated. The ADC map (leftmost), D map (second left), D* map (second right), and f map (rightmost) are automaticallyand instantly calculated (F). G Displays a b = 0 DWI image automatically overlaid on four maps

pseudo-diffusion coefficient (D*, ­mm2/s), in one voxel
automatically and instantly (Fig. 1F).
We adapted the biexponential IVIM fitting method
using the Levenberg–Marquardt algorithm with a constraint on f (0 < f < 1) as following equation:


 

S
= f · exp −bD∗ + 1 − f · exp(−bD)
S0where S is the signal intensity at a given b value, S0 is
the signal intensity at b = 0 s/mm2. After IVIM analysis,b = 0 DWI images were automatically superimposed on
the f maps as shown in Fig. 1G. The average, maximum,and minimum values of f were measured in the following 45 ROIs based on anatomical features: the foramen magnum; bilateral foramina of Luschka; foramenof Magendie; fourth ventricle; lower and upper parts ofthe cerebral aqueduct; anterior and posterior parts ofthe third ventricle; bilateral foramina of Monro; anterior
horn, body, trigone, and inferior horn of the bilateral lateral ventricles; bilateral cerebellopontine angle; prepontine cistern; interpeduncular cistern; lamina terminalis
cistern; suprasellar or chiasmatic cistern; quadrigeminal
cistern; bilateral ambient cisterns; bilateral carotid cisterns; anterior and posterior rami of the bilateral Sylvian
sulci; bilateral Sylvian fossae; lower and upper parts of
the interhemispheric fissure; bilateral superior frontal
sulci; bilateral central sulci; and bilateral marginal sulci.
Fluid oscillation index

After comprehensive evaluation of the relationshipbetween parameters on 4D flow MRI and f-values on
IVIM MR, we developed a new parameter for fluid oscillation, the Fluid Oscillation Index (FOI), by integratingthese two measurements.

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Table 1 Clinical characteristics of the study population
Healthy volunteers

Hakim patients

P1

P2< 40 years

40–59 years≥ 60 years

Total number

45

46

36

44

Sex (female:male)

30:15

32:14

21:15

28:16

0.071

0.550

Age (years)

29.2 ± 5.4

49.4 ± 5.8

69.2 ± 6.9

77.1 ± 7.0< 0.001< 0.001

Evans index

0.25 ± 0.02

0.25 ± 0.02

0.26 ± 0.02

0.37 ± 0.18

< 0.001

0.045

Z-Evans index

0.24 ± 0.03

0.24 ± 0.03

0.27 ± 0.04

0.43 ± 0.14

< 0.001< 0.001

BVR at AC

2.1 ± 0.3

2.1 ± 0.3

1.8 ± 0.4

0.83 ± 0.27

< 0.001< 0.001

BVR at PC

4.8 ± 1.4

4.3 ± 1.1

3.0 ± 1.0

0.99 ± 0.22

< 0.001< 0.001

Callosal angle (degree)

119.9 ± 9.4

118.7 ± 11.4

117.0 ± 11.5

68.9 ± 16.7

< 0.001

0.614

Area of the foramen of Magendie

13.3 ± 911.3 ± 5.8

18.5 ± 10.5

41.8 ± 21.6

< 0.001

0.002

Diameter of the foramen of Magendie

3.1 ± 1.4

2.8 ± 13.5 ± 1.2

6.7 ± 2.5

< 0.001

0.104

Diameter of the right foramen of Luschka

1.4 ± 0.8

1.7 ± 0.9

1.7 ± 1.0

3.4 ± 1.5

< 0.001

0.290

Diameter of the left foramen of Luschka

1.7 ± 0.9

2.3 ± 1.2

2.2 ± 1.2

3.7 ± 1.4

0.002

0.014

Total ventricles (mL)

19.5 ± 7.5

22 ± 8.2

36.4 ± 14.3

127 ± 33.5

< 0.001< 0.001

Total ventricles (%)

1.3 ± 0.5

1.5 ± 0.5

2.5 ± 0.9

8.4 ± 1.8

< 0.001< 0.001

Total subarachnoid spaces (mL)

267 ± 35.7

287 ± 43.2

352 ± 55.0

325 ± 44.6

0.032

< 0.001

Total subarachnoid spaces (%)

18.2 ± 2

20.1 ± 2.3

24.5 ± 2.7

21.6 ± 2.5< 0.001< 0.001

Total intracranial CSF space (mL)

287 ± 39.1

309 ± 47.8

388 ± 64.7452 ± 56.2

< 0.001< 0.001

Total intracranial CSF space (%)

19.5 ± 2.2

21.6 ± 2.4

27.0 ± 3.0

30.0 ± 2.5< 0.001< 0.001

P1; probability value for the Mann–Whitney–Wilcoxon test between patients with Hakim’s disease and healthy volunteers aged ≥ 60 years
P2; probability value for the Kruskal–Wallis test among the three age groups of healthy volunteers
Sex differences were compared using Fisher’s exact test
AC anterior commissure, PC posterior commissure, CSF cerebrospinal fluid, BVR brain per ventricle ratio, AC anterior commissure, PC posterior commissure

Measurements and indices specific to Hakim’s disease

The total ventricles and subarachnoid spaces were manually segmented from the 3D T2-weighted cube sequence
using our original method combined with a simple
threshold algorithm and manual segmentation, as previously reported [33, 34, 37]. Furthermore, the area andmaximum anteroposterior diameter of the foramen of
Magendie and the maximum anteroposterior diameterof the bilateral foramina of Luschka were measured. The
indices specific to Hakim’s disease—the Evans index
defined as the maximal width of the frontal horns of
the lateral ventricles to the maximal width of the internal diameter of the cranium based on the x-dimension;
Z-Evans index defined as the maximum z-axial length of
the frontal horns of the lateral ventricles to the maximum
cranial z-axial length on the coronal plane, which wasperpendicular to the anteroposterior commissure planeon the anterior commissure [33]; brain per ventricle
ratios (BVRs) defined as the maximum width of the brain
just above the lateral ventricles divided by the maximum
width of the lateral ventricles on the reference coronalplanes at the anterior commissure and posterior commissure levels, respectively [34]; and callosal angle definedas the angle of the roof of the bilateral ventricles on thecoronal plane at the posterior commissure level [38]—were measured.
Statistical analysis

The volunteers were divided into the following threesubgroups according to their ages at the time of MRI
examination: < 40 years, 40–59 years, and ≥ 60 years.
The mean ± standard deviation (SD) for several measurements and indices in the three age subgroups werecompared using the Kruskal–Wallis rank sum test,
and those in patients with Hakim’s disease and healthyelderly aged ≥ 60 years were compared using the Mann–
Whitney–Wilcoxon test. The chi-square test was used
to compare the proportions of the groups. The relationship between the mean f-values on IVIM MRI and flow
parameters measured on 4D flow MRI, such as VA, absolute maximum velocity, mean velocity, stroke volume, net
flow volume, and reverse flow rate at the following seven
ROIs: left and right foramina of Monro, upper and lower
parts of the cerebral aqueduct, foramen of Magendie,
dorsal part of the foramen magnum, and prepontine cistern. These parameters were investigated using Pearson’s
correlation coefficient (r) and 95% confidential intervals(CIs). The ROI of the lower part of the cerebral aqueduct
for 4D flow MRI was treated as the ROI of the upper part

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Fig. 2 Four-dimensional flow magnetic resonance imaging. The axial (A) sagittal (B) and coronal (C) colormap views of 4D flow MRI analysis
show the velocity magnitude of CSF movements around the cerebral aqueduct (white arrowheads) in a representative patient with Hakim’s
disease (73 years old, female). The colored area indicates the area where the 3D flow velocity (cm/s) was measured on 4D flow MRI, and the color
bar indicates the magnitude of the 3D velocity of reciprocating CSF movements: red for fast and blue for slow. The 2D sagittal views of flow
vectors (D–F) show the CSF movements with direction and velocity. The color and size of arrows indicate the velocity magnitude. The
3D view of streamlines (G) shows the CSF movements thorough seven ROIs during a heartbeat (a movie of the streamlines was attachedto the supplemental materials). The line graph H shows the flow velocity and the line graph I shows the flow volume at seven ROIs. The ROIs
were manually drawn at seven points, including the dorsal aspects of the foramen magnum (ROI1), foramen of Magendie (ROI2), lower (ROI3)
and upper (ROI4) parts of the cerebral aqueduct, left (ROI5) and right (ROI6) foramina of Monro, and prepontine cistern (ROI7). The line graphs show
the temporal changes of flow velocity (H) and flow volume (I) at these ROIs during the cardiac cycle

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Fig. 3 Intravoxel incoherent motion f-map in a patient with Hakim’s disease and healthy control. The upper A–F figures show the f-maps
focused on seven ROIs in the same patient with Hakim’s disease as Fig. 2, and the lower G–L figures show the f-maps in a representative healthy
volunteer with the same age and sex as the patient with Hakim’s disease (73 years old, female). The ROIs were manually drawn at seven points,
including the dorsal aspects of the foramen magnum (ROI1) in A and G, foramen of Magendie (ROI2) in B and H, lower part of the cerebral
aqueduct (ROI3) in C and I, upper part of the cerebral aqueduct (ROI4) in D and J, left (ROI5) and right (ROI6) foramina of Monro in E and K,
and prepontine cistern (ROI7) in F and L. The color bar indicates the magnitude of the f-values on IVIM MRI: red for fast and blue for slow

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Table 2 Stroke volume (μL/min) and velocity amplitude (VA) (cm/s) on 4D flow MRI and f (%) on IVIM MRIRegion of interest

Stroke volume (μL/min)

VA (cm/s)f (%)

Foramen magnum, mean ± SD

237 ± 145

1.33 ± 0.61

70.2 ± 16.6

(Range)(40–1100)(0.20–4.85)(9.2–94.6)

Prepontine cistern, mean ± SD

162 ± 104

1.27 ± 0.72

87.3 ± 4.5(Range)(20–600)(0.27–6.37)(65.9–95.4)

Foramen of Magendie, mean ± SD

27.2 ± 23.3

0.49 ± 0.32

76.4 ± 8.4(Range)(0–150)(0.10–2.14)(36.6–93.4)

Lower part of cerebral aqueduct, mean ± SD

33.0 ± 29.6

0.78 ± 0.50

75.6 ± 7.5(Range)(0–220)(0.14–3.40)(45.5–89.9)

Upper part of cerebral aqueduct, mean ± SD

32.0 ± 35.2

0.94 ± 0.70

71.2 ± 11.1

(Range)(0–170)(0.07–3.53)(30.6–89.7)

Left foramen of Monro, mean ± SD

34.3 ± 34.0

0.48 ± 0.26

74.7 ± 12.9

(Range)(0–220)(0.07–1.56)(29.9–92.8)

Right foramen of Monro, mean ± SD

29.1 ± 24.7

0.41 ± 0.26

72.8 ± 14.2

(Range)(0–120)(0.05–1.67)(8.6–92.5)

Fig. 4 Distribution of f on IVIM MRI and velocity amplitude (VA) on 4D flow MRI. The scatter plot shows the relationship between the f (%)
on IVIM MRI (x axis) and VA (cm/s) on 4D flow MRI (y axis). The color plots and lines indicate the healthy group (salmon pink) and patientswith Hakim’s disease (sky blue). Filled-in points represent actual measurements, while hollowed-out points represent estimates calculated using
the following formula: e0.2(x−85) + 0.25. The dot vertical line indicates f = 75%, horizontal line VA = 0.4 cm/sof the fourth ventricle for IVIM MRI. In addition, weinvestigated the relationship between the measurements
and indices specific to Hakim’s disease and the FOIs in
the entire intracranial space. Statistical significance wasassumed at a probability (P) value of < 0.05. All missing
data points were treated as deficit data that did not affectother variables. Statistical analyses were performed using

R (version 4.2.3; The R Foundation for Statistical Computing; http://​www.R-​proje​ct.​org).

Results
Clinical characteristics

In this study, 127 healthy volunteers (< 40 years, n = 45;
40–59 years, n = 46; ≥ 60 years, n = 36), including 44 males

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Fig. 5 Distribution of fluid oscillation index (FOI) among healthy controls and Hakim patients. Each graph is a combination of violin plots
for the distribution of the VA and line graphs for the mean volume in each age group of healthy volunteers and Hakim group stratified according
to sex. Salmon pink indicates female, sky blue indicates male, and black indicates all. The vertical lines contain the volumes between the 25thand 75th percentiles. The mean FOI in each group is shown above the violin plot

and 83 females, and 44 Hakim’s patients (77.1 ± 7.0 years),
including 28 males and 16 females, were included. The
linear and volumetric indices for the size of the intracranial CSF space, including the ventricular systems, among
the three age groups of healthy volunteers and patientswith Hakim’s disease are summarized in Table 1. Significant differences in all measurements were observed
between patients with Hakim’s disease and healthyelderly individuals aged ≥ 60 years.

Relationship between flow parameters on 4D flow MRI
and f‑values on IVIM MRI

The flow parameters on 4D flow MRI at seven ROIs
(Fig. 2 and supplemental movie) were comprehensively
assessed to determine their relationship with the mean
f-values on IVIM MRI at the same location (Fig. 3).
Table 2 shows the averages and ranges of stroke volume and VA measured by 4D Flow MRI and f-values
measured by IVIM MRI at 7 ROIs. Based on these averages and ranges of these parameters, we were unable toYamada et al. Fluids and Barriers of the CNS(2024) 21:47

determine which specific 4D flow MRI parameters correspond to the f-values optimally.
Estimation of CSF movement from the f‑values on IVIM MRI
and fluid oscillation index (FOI)

In order to elucidate the optimal 4D flow MRI parameters corresponding to f on IVIM MRI, a scatter plot wasconstructed to visualize the distribution of all f-values
and VAs measured at 7 ROIs (Fig. 4).
Based on this distribution, it was deemed inappropriate
to employ a linear regression model, and instead, fitting
the data to a nonlinear exponential curve was considered to be the optimal approach. The exponential curveof e0.2(x−85) + 0.25 was derived to best fit the distribution,
providing a more accurate representation of the relationship between the f-values and VAs. From this regression equation, it was determined that VA corresponds
to 0.4 cm/s when f is 75%. The utility of f surpassed thatof VA in assessing flow oscillations when f-values were
below 75%; conversely, VA proved more informative
when f exceeded 75%. Consequently, in light of thesefindings, we introduced a novel index, FOI, calculated
as VA (or estimated VA) × 10 + f × 0.02, which integrates
these distinct parameters to quantitatively evaluate fluid
oscillation. The distributions of FOIs across the entire
intracranial space in the three age groups of healthy controls and the Hakim group are depicted in violin plots
by sex (Fig. 5). FOIs were relatively large in the posterior
subarachnoid space, foramen magnum, cerebral aqueduct, third ventricle, and Sylvian fossa, and very low inthe lateral ventricles and the high parietal convexity partof the subarachnoid spaces.
Relationship between measurements and indices specificto Hakim’s disease and fluid oscillation index (FOI)

We comprehensively examined the relationship between
the morphological characteristics of Hakim’s disease and
FOIs at 45 ROIs using correlation matrices. As shown inPage 10 of 15

Fig. 6, the strongest correlations were observed between
FOI at the left foramen of Luschka (ROI3) and the Evansindex (r, 0.76; 95% CIs, 0.69 to 0.82; p < 0.001). The otherindices related to Hakim’s disease including ventricular volume ratio (%) were significantly associated with
the FOIs at the cerebral aqueduct (ROI7 and 8) and thebilateral foramina of Luschka (ROI2 and 3), rather than
the foramen of Magendie (ROI4). The ROIs showing significant correlations with ventricular volume ratio (%) in
Fig. 6 are summarized in Table 3.

Discussion
In this study, we proposed a novel method for estimating CSF motion throughout the entire intracranial CSF
space by integrating flow velocity data from 4D flow
MRI and f from IVIM MRI (Fig. 7). Although 4D flow
MRI can three-dimensionally measure the velocity anddirection of CSF motions, the range of velocity measurement depends on velocity encoding. Conversely,
IVIM MRI could not directly measure the velocity anddirection of CSF motions; however, it could visualize
and quantify very small CSF motions that could not bemeasured using 4D flow MRI. Furthermore, IVIM MRI
can provide an overview of all CSF movements driven
by arterial pulsations, brain pulsations produced by
the cerebral blood circulation, respiration, and coordinated directional beating of the motile cilia, except forhead movements, because it does not require heartbeat or respiration synchronization. Therefore, weinvestigated whether the flow information from thetwo MRI methods could be used to simulate the overall intracranial flow.
From the distribution of VA on 4D Flow MRI and f on
IVIM MRI in Fig. 4, VA, reciprocating bidirectional CSF
motion or oscillation, should to be evaluated with a focus
on VA rather than f, when the VA exceeds 0.4 cm/s. Conversely, when the VA is below 0.4 cm/s, f rather than VA
would be better suited for evaluating reciprocating CSF(See figure on next page.)
Fig. 6 Correlation matrix between morphological parameters for Hakim’s disease and fluid oscillation index (FOI) in the entire intracranial CSF
space. The correlation matrix is a comprehensive correlation analysis between parameters that indicate the morphological characteristics of Hakim’s
disease and FOI. As shown in the color bar on the right, the red gradient indicates the strength of the positive correlation, whereas the blue gradientindicates the strength of the negative correlation. Furthermore, the numbers written inside were Pearson’s correlation coefficients. F foramen, SAS
subarachnoid space, CSF cerebrospinal fluid. The numbers of regions of interests (ROIs) indicate the following regions: the foramen magnum (ROI1),
right and left foramina of Luschka (ROI2 and 3), foramen of Magendie (ROI4), lower part of 4th ventricle (ROI5), 4th ventricle (ROI6), lower partof cerebral aqueduct (ROI7), upper part of cerebral aqueduct (ROI8), anterior part of 3rd ventricle (ROI9), posterior part of cerebral aqueduct (ROI10),
right and left foramina of Monro (ROI11 and 12), right and left lateral ventricles (anterior horn ROI13 and 14, body ROI15 and 16, trigone ROI17
and 18, and inferior horn ROI19 and 20), right and left cerebellopontine angle (ROI21 and 22), prepontine cistern (ROI23), interpeduncular cistern(ROI24), lamina terminalis cistern (ROI25), suprasellar or chiasmatic cistern (ROI26), quadrigeminal cistern (ROI27), right and left ambient cisterns(ROI28 and 29), right and left carotid cisterns (ROI30 and 31), right and left Sylvian sulci (anterior ROI32 and 33, and posterior ROI36 and 37), rightand left Sylvian fossae (ROI34 and 35), basal interhemispheric fissure (ROI38), upper part of interhemispheric fissure (ROI39), right and left superior
frontal sulci (ROI40 and 41), right and left central sulci (ROI42 and 43), and right and left marginal sulci (ROI44 and 45)

Yamada et al. Fluids and Barriers of the CNS

Fig. 6 (See legend on previous page.)(2024) 21:47

Page 11 of 15

Yamada et al. Fluids and Barriers of the CNS(2024) 21:47

Table 3 Pearson’s correlation coefficient (r) and 95% confidential
intervals (95% CIs) for the relationship between ventricularvolume ratio (%) and fluid oscillation index (FOI)ROI number ROI namer (95% CIs)

ROI2

Rt. foramen of Luschka

0.44 (0.32–0.56)

ROI3

Lt. foramen of Luschka

0.37 (0.24–0.50)

ROI7

Lower end of cerebral aqueduct

0.39 (0.26–0.51)

ROI8

Upper end of cerebral aqueduct

ROI10

3rd ventricle (anterior part)− 0.32 (− 0.45 to − 0.18)

ROI21

Rt. cerebellopontine angle− 0.33 (− 0.46 to − 0.19)

ROI26

Suprasellar cistern− 0.39 (− 0.51 to − 0.26)

ROI29

Lt. ambient cistern− 0.41 (− 0.53 to − 0.28)

ROI30

Rt. carotid cistern− 0.35 (− 0.47 to − 0.21)

ROI31

Lt. carotid cistern− 0.30 (− 0.43 to − 0.15)

0.43 (0.30–0.54)

motion. Based on the relationship between VA and f, a
new FOI was created to comprehensively evaluate CSF
oscillations by integrating both variables.
The major driving force of CSF movements is the pulsation of the brain due to cerebral circulation [2, 8, 20, 28,39]. Brain pulsation generates and propagates waves in
the CSF, and pressure gradients cause the CSF to move.
Age-related changes in the intracranial environment
require consideration of not only a decrease in brain volume [40, 41] but also an increase in CSF in the ventricles
and subarachnoid space, a decrease in the total cerebral

Page 12 of 15

blood flow volume due to a decrease in brain volume[42], and a further decrease in pulsatile CSF movements
due to a decrease in the cerebral blood flow volume.
Compared with healthy aging volunteers, patients with
Hakim’s disease had a significantly larger intracranial CSF
volume, which is distributed disproportionately, such as
ventricular dilatation, enlarged Sylvian fissure, and narrow sulci at high parietal convexity; these are collectively
called DESH [32–34]. The reasons for the development of
DESH, particularly the narrow sulci at high parietal convexity, remain to be fully elucidated. We observed that
the FOI in the upper part of the cerebral aqueduct was
similarly elevated in patients with Hakim’s disease andhealthy elderly individuals aged ≥ 60 years. However, wefound that the FOI in the third ventricle, particularly inthe anterior part, was significantly lower in patients with
Hakim’s disease compared to healthy elderly individuals.
This difference may be attributed to alterations in CSF
dynamics in the third ventricle, where a laminar to vortex flow pattern is established. Furthermore, we foundthe morphological features of DESH were significantly
associated with the FOIs at the cerebral aqueduct andbilateral foramina of Luschka, rather than the foramen of
Magendie. In our previous studies [6], we concluded thatthe enlargement of the foramen of Magendie with aging
results in the transmission of CSF pulses into the ventricles, leading to ventricular enlargement. However, wehave now discovered that fluid oscillations at the bilateralFig. 7 Illustration of CSF movement in a healthy control and a patient with Hakim’s disease. Compared to healthy controls, patients with Hakim’s
disease have larger CSF repetitive movements measured by velocity amplitude (VA) on 4D Flow MRI at the cerebral aqueduct and vortex flowin the third ventricle (red arrows) and smaller CSF repetitive movements measured by f on IVIM MRI in the superficial subarachnoid space (orange
arrows)Yamada et al. Fluids and Barriers of the CNS(2024) 21:47

foramina of Luschka may be more closely associated with
DESH formation, including ventricular enlargement,
compared to those at the foramen of Magendie. CSF
movements through the foramen magnum, which is theepicenter of CSF movements, play a dual role: to decreasepressure fluctuations associated with changes in the volume of blood inflow and outflow in the intracranial space
during cerebral circulation and to regulate intracranialpressure during positional changes between supine andupright postures [19, 43]. This role requires an extensive spinal subarachnoid space contiguous to the lumbar
region, where the CSF moves in and out of the skull basethrough the foramen magnum. Pulsatile CSF movements
require energy according to the law of conservationof energy, and energy consumption can be minimized
as much as possible by collecting the CSF near the epicenter, which is the foramen magnum. As CSF increases,
it may accumulate close to the foramen magnum, where
it is most mobile. Conversely, the supratentorial convexity region located farthest from the foramen magnum,
where the CSF is least mobile, may become compressed,
resulting in the characteristic morphology of DESH.
We observed an increase in pulsatile CSF motions in
the Sylvian fossa, carotid cistern, and ambient cisterns,
which contain the major intracranial arteries aroundthe circle of Willis, with aging. However, there was anotable decrease in CSF oscillations in Hakim’s disease.
Arterial pulsatility tends to increase with age due to aortic stiffness [44–48], but CSF oscillations due to arterial
pulsation may be attenuated in Hakim’s disease withenlargement of the Sylvian fissure and basal cistern.
Aging and various diseases such as hypertension anddiabetes have been reported as risk factors for Hakim’s
disease [8, 49–51], which is known to be caused by arteriosclerosis affecting the dilation and contraction of cerebral arteries [44]. Furthermore, patients with Hakim’s
disease exhibit shorter and fewer perivascular spaces
as well as severe periventricular hyperintensity on MRI
compared to healthy controls [52]. Additionally, theseabnormal features have been observed to improve after
CSF shunt surgery [52]. These findings may be related todysfunction of the glymphatic system [17, 18] in Hakim’s
disease. However, the relationship between CSF flow andarterial wall pulsation has not been fully elucidated.
This study has some limitations. First, the distributionsof sex and age in the healthy controls aged ≥ 60 years did
not match those in the Hakim group. The mean age of the
Hakim group was significantly higher than that of healthy
controls aged ≥ 60 years. A sufficient number of older
controls could have more clearly shown the difference inf-values between brains with Hakim’s disease and healthy
aging brains. Second, the ROIs on 4D flow MRI andPage 13 of 15

IVIM MRI were manually placed by a single researcher
based on anatomical features; therefore, reproducibility was not ensured. We would like to enable automatic
ROI placement in the application. Third, the correlation
between the flow velocity parameters on 4D flow MRI
and the mean f-values on IVIM MRI was not very strong.
Additionally, VA = 0.4 cm/s was assumed to correspondto f = 75% in this study; however, these values may be
influenced by sequence parameters such as the venc setting for 4D Flow MRI and the b-values setting for IVIM
MRI. Finally, we applied a nonlinear exponential curve
regression equation to estimate VA from the mean f-values on IVIM MRI. In the future, age-related changes in
CSF dynamics in the entire intracranial CSF space should
be simulated using advanced computational fluid dynamics data assimilation of flow velocity data obtained using
4D flow MRI and IVIM MRI to elucidate the mechanismof disproportionate CSF distribution, including DESH, in
Hakim’s disease. Data assimilation using computational
fluid dynamics and lattice models is most effective in situations where complex fluid motions must be predicted.

Conclusions
We comprehensively investigated the CSF dynamics inhealthy individuals with ages ranging from their 20 s to
80 s and patients with Hakim’s disease (iNPH) by integrating 4D flow MRI and IVIM MRI. We estimated thefluid oscillations in the entire intracranial CSF space
using a novel index, the FOI, which combined VA derived
from 4D flow MRI with f-values derived from IVIM MRI.
In addition, we found the ventricle volume ratio and morphological features of Hakim’s disease were significantly
associated with the FOIs at the cerebral aqueduct andbilateral foramina of Luschka, rather than the foramen of
Magendie. Furthermore, FOIs at the cerebral aqueduct
and bilateral foramina of Luschka were elevated in bothHakim’s disease and healthy controls aged ≥ 60 years.
The computational simulation by estimating CSF motion
across the entire intracranial CSF space on both 4D flow
MRI and IVIM MRI could help explore the underlying
mechanisms of disproportionate expansion of the ventricles and subarachnoid spaces in Hakim’s disease.
Abbreviations
BVR	Brain per ventricle ratio
CSF	Cerebrospinal fluid
DESH	Disproportionately enlarged subarachnoid space hydrocephalus
DWI	Diffusion weighted imagef	Fraction of incoherent perfusion
FOI	Fluid oscillation index
IVIM	Intravoxel incoherent motion
PC	Phase-contrast
ROI	Region of interest
SD	Standard deviation
VA	Velocity amplitude

Yamada et al. Fluids and Barriers of the CNS(2024) 21:47

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12987-​024-​00552-6.
Supplementary Material 1.
Acknowledgements
We thank the radiology staff of Shiga University of Medical Science andNagoya City University Hospital. We thank Enago (www.​enago.​jp) for the
English language review. We are grateful to FUJIFILM Corporation for allowing
us to use the pre-released version of the SYNAPSE 3D workstation.
Author contributions
Shigeki Yamada: conceptualization, methodology, data curation, formal
analysis, investigation, resources, project administration, writing—originaldraft, and visualization. Tomohito Otani: resources, project administration, andsupervision. Satoshi Ii: resources, project administration, and supervision. Hirotaka Ito: developer of applications for analysis, methodology, and supervision.
Chifumi Iseki: supervision. Motoki Tanikawa: supervision. yoshiyuki watanabe:
resources, project administration, and supervision. Shigeo Wada: resources,
project administration, and supervision. Marie Oshima: resources, project
administration, and supervision. Mitsuhito Mase: supervision.
Funding
Shigeki Yamada has received research grants from the Japan Society for
the Promotion of Science (JSPS) Grant-in-Aid for Scientific Research (C)for 3 years, beginning in 2021 (Grant No. 21K09098); JSPS Grant-in-Aid for
Scientific Research (A) for 4 years, beginning in 2022 (Grant No. 22H00190);
JSPS Grant-in-Aid for Scientific Research (B) for 3 years, beginning in 2022
(Grant no. 22H03020); JSPS Grant-in-Aid for Scientific Research (B) for 4 years,
beginning in 2024 (Grant No. 24K02557); from the Ministry of Education,
Culture, Sports, Science and Technology as “Program for Promoting Researcheson the Supercomputer Fugaku” for 4 years, beginning in 2023 (Grant No.
DJPMXP1020230118); FUJIFILM Corporation for 4 years, beginning in 2019; G-7
Scholarship Foundation for 1 year in 2020, Osaka Gas Group Welfare Foundation for 1 year in 2022, Taiju Life Social Welfare Foundation for 1 year in 2020,and for 1 year in 2022. The funding sources for the study played no role in thedesign and conduct of the study, in the collection, analysis, and interpretationof the data, or in the preparation, review, or approval of the manuscript.
Availability of data and materials
The data, including clinical information, collected and analyzed in this study
will only be available if the Ethics Committees approve new participation in
the collaborative research.

Declarations
Ethics approval and consent to participate
The study design and protocol of this prospective and observational study
were approved by the Ethics Committees for Human Research of our institutions (IRB Number: 60-22-0083 and R2019-227). Healthy volunteers underwent
MRI after providing written informed consent, and we particularly explained
the potential for detecting diseases in the brain. The patients’ MRI data wereobtained using an opt-out method. All MRI data were extracted after the
personal information of the study subjects was anonymized in a linkable manner. The study design was prospective and observational. This study was conducted according to the approved guidelines of the Declaration of Helsinki.
Consent for publication
Not applicable.
Competing interests
Some authors of this manuscript declare their relationship with FUJIFILM
Corporation because this study used a SYNAPSE 3D workstation (FUJIFILM Corporation; Tokyo, Japan), as a medical device approved by the Pharmaceuticals
and Medical Devices Agency of Japan in 2020. Shigeki Yamada has received
research grants from the FUJIFILM Corporation and speakers’ honoraria from
FUJIFILM Medical Systems since 2019. Hirotaka Ito is employed by the FUJIFILM

Page 14 of 15

Corporation and has made substantial contributions to the development of
the applications of the SYNAPSE 3D workstation. The other authors of thismanuscript declare that they have no relationships with any companies whose
products or services may be related to the subject matter of the article.
Author details
1
Department of Neurosurgery, Nagoya City University Graduate Schoolof Medical Science, Kawasumi 1, Mizuho‑Cho, Mizuho‑Ku, Nagoya, Aichi
467‑8601, Japan. 2 Interfaculty Initiative in Information Studies/Instituteof Industrial Science, The University of Tokyo, Tokyo, Japan. 3 Departmentof Mechanical Science and Bioengineering, Graduate School of Engineering
Science, Osaka University, Osaka, Japan. 4 Department of Mechanical Engineering, School of Engineering, Tokyo Institute of Technology, Tokyo, Japan. 5 Faculty of System Design, Tokyo Metropolitan University, Tokyo, Japan. 6 Medical
System Research & Development Center, FUJIFILM Corporation, Tokyo, Japan.
7
Department of Behavioural Neurology and Cognitive Neuroscience, Tohoku
University Graduate School of Medicine, Sendai, Miyagi, Japan. 8 Divisionof Neurology and Clinical Neuroscience, Department of Internal Medicine
III, Yamagata University School of Medicine, Yamagata, Japan. 9 Departmentof Radiology, Shiga University of Medical Science, Shiga, Japan.
Received: 17 December 2023 Accepted: 21 May 2024

References
1. Ohata S, Nakatani J, Herranz-Perez V, Cheng J, Belinson H, Inubushi T, et al.
Loss of Dishevelleds disrupts planar polarity in ependymal motile cilia
and results in hydrocephalus. Neuron. 2014;83(3):558–71.
2. Olstad EW, Ringers C, Hansen JN, Wens A, Brandt C, Wachten D, et al. Ciliary beating compartmentalizes cerebrospinal fluid flow in the brain andregulates ventricular development. Curr Biol. 2019;29(2):229-241.e6.
3. Bradley WG, Queralt SD. Normal-pressure hydrocephalus: evaluation
with cerebrospinal fluid flow measurements at MR imaging. Radiology.
1996;198:523–9.
4. Bradley WG. CSF flow in the brain in the context of normal pressurehydrocephalus. Am J Neuroradiol. 2015;36(5):831–8.
5. Kahlon B, Annertz M, Stahlberg F, Rehncrona S. Is aqueductal stroke volume, measured with cine phase-contrast magnetic resonance imaging
scans useful in predicting outcome of shunt surgery in suspected normalpressure hydrocephalus? Neurosurgery. 2007;60(1):124–9 (discussion9–30).
6. Yamada S, Ishikawa M, Ito H, Yamamoto K, Yamaguchi M, Oshima M, et al.
Cerebrospinal fluid dynamics in idiopathic normal pressure hydrocephalus on four-dimensional flow imaging. Eur Radiol. 2020;30(8):4454–65.
7. Yamada S, Ito H, Ishikawa M, Yamamoto K, Yamaguchi M, Oshima M, et al.
Quantification of oscillatory shear stress from reciprocating CSF motion
on 4D flow imaging. Am J Neuroradiol. 2021;42(3):479–86.
8. Yamada S, Ishikawa M, Nozaki K. Exploring mechanisms of ventricular enlargement in idiopathic normal pressure hydrocephalus: a roleof cerebrospinal fluid dynamics and motile cilia. Fluids Barriers CNS.
2021;18(1):20.
9. Chen L, Beckett A, Verma A, Feinberg DA. Dynamics of respiratory andcardiac CSF motion revealed with real-time simultaneous multi-slice EPI
velocity phase contrast imaging. Neuroimage. 2015;122:281–7.
10. Lagana MM, Di Tella S, Ferrari F, Pelizzari L, Cazzoli M, Alperin N, et al.
Blood and cerebrospinal fluid flow oscillations measured with real-timephase-contrast MRI: breathing mode matters. Fluids Barriers CNS.
2022;19(1):100.
11. Yamada S, Miyazaki M, Kanazawa H, Higashi M, Morohoshi Y, Bluml S, et al.
Visualization of cerebrospinal fluid movement with spin labeling at MR
imaging: preliminary results in normal and pathophysiologic conditions.
Radiology. 2008;249(2):644–52.
12. Uribe S, Beerbaum P, Sorensen TS, Rasmusson A, Razavi R, Schaeffter T.
Four-dimensional (4D) flow of the whole heart and great vessels using
real-time respiratory self-gating. Magn Reson Med. 2009;62(4):984–92.
13. Lindstrøm EK, Ringstad G, Mardal K-A, Eide PK. Cerebrospinal fluid
volumetric net flow rate and direction in idiopathic normal pressurehydrocephalus. NeuroImage Clin. 2018;20:731–41.

Yamada et al. Fluids and Barriers of the CNS(2024) 21:47

14. Odeen H, Uppman M, Markl M, Spottiswoode BS. Assessing cerebrospinal
fluid flow connectivity using 3D gradient echo phase contrast velocity
encoded MRI. Physiol Meas. 2011;32(4):407–21.
15. Rivera-Rivera LA, Vikner T, Eisenmenger L, Johnson SC, Johnson KM. Fourdimensional flow MRI for quantitative assessment of cerebrospinal fluid
dynamics: status and opportunities. NMR Biomed. 2023. https://​doi.​org/​
10.​1002/​nbm.​5082.
16. Takizawa K, Matsumae M, Hayashi N, Hirayama A, Yatsushiro S, Kuroda K.
Hyperdynamic CSF motion profiles found in idiopathic normal pressurehydrocephalus and Alzheimer’s disease assessed by fluid mechanics derived from magnetic resonance images. Fluids Barriers CNS.
2017;14(1):29.
17. Mestre H, Tithof J, Du T, Song W, Peng W, Sweeney AM, et al. Flow of
cerebrospinal fluid is driven by arterial pulsations and is reduced in
hypertension. Nat Commun. 2018;9(1):4878.
18. Nedergaard M, Goldman SA. Glymphatic failure as a final common pathway to dementia. Science. 2020;370(6512):50–6.
19. Baledent O, Henry-Feugeas MC, Idy-Peretti I. Cerebrospinal fluid
dynamics and relation with blood flow: a magnetic resonance study
with semiautomated cerebrospinal fluid segmentation. Invest Radiol.
2001;36(7):368–77.
20. Yamada S, Ito H, Tanikawa M, Ii S, Otani T, Wada S, et al. Age-relatedchanges in cerebrospinal fluid dynamics in the pathogenesis of chronichydrocephalus in adults. World Neurosurg. 2023;178:351–8.
21. Iliff JJ, Wang M, Liao Y, Plogg BA, Peng W, Gundersen GA, et al. A paravascular pathway facilitates CSF flow through the brain parenchyma and theclearance of interstitial solutes, including amyloid beta. Sci Transl Med.
2012;4(147):147ra11.
22. Nedergaard M. Neuroscience. Garbage truck of the brain. Science.
2013;340(6140):1529–30.
23. Xie L, Kang H, Xu Q, Chen MJ, Liao Y, Thiyagarajan M, et al. Sleep
drives metabolite clearance from the adult brain. Science.
2013;342(6156):373–7.
24. Kockum K, Lilja-Lund O, Larsson EM, Rosell M, Soderstrom L, Virhammar J,
et al. The idiopathic normal-pressure hydrocephalus Radscale: a radiological scale for structured evaluation. Eur J Neurol. 2018;25(3):569–76.
25. Sasaki M, Honda S, Yuasa T, Iwamura A, Shibata E, Ohba H. Narrow CSF
space at high convexity and high midline areas in idiopathic normal pressure hydrocephalus detected by axial and coronal MRI. Neuroradiology.
2008;50(2):117–22.
26. Yamada S, Ito H, Matsumasa H, Tanikawa M, Ii S, Otani T, et al. Tightened
sulci in the high convexities as a noteworthy feature of idiopathic normalpressure hydrocephalus. World Neurosurg. 2023;176:e427–37.
27. Tullberg M, Toma AK, Yamada S, Laurell K, Miyajima M, Watkins LD, et al.
Classification of chronic hydrocephalus in adults: a systematic review andanalysis. World Neurosurg. 2024;183:113–22.
28. Yamada S, Hiratsuka S, Otani T, Ii S, Wada S, Oshima M, et al. Usefulness of
intravoxel incoherent motion MRI for visualizing slow cerebrospinal fluid
motion. Fluids Barriers CNS. 2023;20(1):16.
29. Le Bihan D, Breton E, Lallemand D, Grenier P, Cabanis E, Laval-Jeantet M.
MR imaging of intravoxel incoherent motions: application to diffusionand perfusion in neurologic disorders. Radiology. 1986;161(2):401–7.
30. Le Bihan D, Breton E, Lallemand D, Aubin ML, Vignaud J, Laval-Jeantet M.
Separation of diffusion and perfusion in intravoxel incoherent motion MR
imaging. Radiology. 1988;168(2):497–505.
31. Le Bihan D. What can we see with IVIM MRI? Neuroimage.
2019;187:56–67.
32. Hashimoto M, Ishikawa M, Mori E, Kuwana N. Diagnosis of idiopathic
normal pressure hydrocephalus is supported by MRI-based scheme: aprospective cohort study. Cerebrospinal Fluid Res. 2010;7(1):1–11.
33. Yamada S, Ishikawa M, Yamamoto K. Optimal diagnostic indices for idiopathic normal pressure hydrocephalus based on the 3D quantitative
volumetric analysis for the cerebral ventricle and subarachnoid space.
Am J Neuroradiol. 2015;36(12):2262–9.
34. Yamada S, Ishikawa M, Yamamoto K. Comparison of CSF distribution
between idiopathic normal pressure hydrocephalus and Alzheimer
disease. Am J Neuroradiol. 2016;37(7):1249–55.
35. Nakajima M, Yamada S, Miyajima M, Ishii K, Kuriyama N, Kazui H, et al.
Guidelines for management of idiopathic normal pressure hydrocephalus(third edition): endorsed by the Japanese society of normal pressurehydrocephalus. Neurol Med Chir. 2021;52(11):775–809.

Page 15 of 15

36. Yavuz Ilik S, Otani T, Yamada S, Watanabe Y, Wada S. A subject-specificassessment of measurement errors and their correction in cerebrospinal fluid velocity maps using 4D flow MRI. Magn Reson Med.
2022;87(5):2412–23.
37. Yamada S, Ishikawa M, Iwamuro Y, Yamamoto K. Choroidal fissure acts asan overflow device in cerebrospinal fluid drainage: morphological comparison between idiopathic and secondary normal-pressure hydrocephalus. Sci Rep. 2016;6:39070.
38. Ishii K, Kanda T, Harada A, Miyamoto N, Kawaguchi T, Shimada K, et al.
Clinical impact of the callosal angle in the diagnosis of idiopathic normalpressure hydrocephalus. Eur Radiol. 2008;18(11):2678–83.
39. Yamada S, Mase M. Cerebrospinal fluid production and absorption andventricular enlargement mechanisms in hydrocephalus. Neurol Med Chir.
2023;63(4):141–51.
40. Yamada S, Otani T, Ii S, Kawano H, Nozaki K, Wada S, et al. Aging-related
volume changes in the brain and cerebrospinal fluid using artificial
intelligence-automated segmentation. Eur Radiol. 2023;33(10):7099–112.
41. Bethlehem RAI, Seidlitz J, White SR, Vogel JW, Anderson KM, Adamson C,
et al. Brain charts for the human lifespan. Nature. 2022;604(7906):525–33.
42. Leenders KL, Perani D, Lammertsma AA, Heather JD, Buckingham P, Healy
MJ, et al. Cerebral blood flow, blood volume and oxygen utilization.
Normal values and effect of age. Brain. 1990;113(Pt 1):27–47.
43. Owashi KP, Capel C, Baledent O. Cerebral arterial flow dynamics duringsystole and diastole phases in young and older healthy adults. Fluids Barriers CNS. 2023;20(1):65.
44. Fonck E, Feigl GG, Fasel J, Sage D, Unser M, Rufenacht DA, et al. Effectof aging on elastin functionality in human cerebral arteries. Stroke.
2009;40(7):2552–6.
45. Lefferts WK, DeBlois JP, Augustine JA, Keller AP, Heffernan KS. Age, sex,
and the vascular contributors to cerebral pulsatility and pulsatile damping. J Appl Physiol. 2020;129(5):1092–101.
46. Smulyan H, Mookherjee S, Safar ME. The two faces of hypertension: roleof aortic stiffness. J Am Soc Hypertens. 2016;10(2):175–83.
47. Tarumi T, Ayaz Khan M, Liu J, Tseng BY, Parker R, Riley J, et al. Cerebral
hemodynamics in normal aging: central artery stiffness, wave reflection,and pressure pulsatility. J Cereb Blood Flow Metab. 2014;34(6):971–8.
48. Zarrinkoob L, Ambarki K, Wahlin A, Birgander R, Carlberg B, Eklund A, et al.
Aging alters the dampening of pulsatile blood flow in cerebral arteries. J
Cereb Blood Flow Metab. 2016;36(9):1519–27.
49. Hudson M, Nowak C, Garling RJ, Harris C. Comorbidity of diabetes mellitus in idiopathic normal pressure hydrocephalus: a systematic literature
review. Fluids Barriers CNS. 2019;16(1):5.
50. Jaraj D, Agerskov S, Rabiei K, Marlow T, Jensen C, Guo X, et al. Vascularfactors in suspected normal pressure hydrocephalus: a population-based
study. Neurology. 2016;86(7):592–9.
51. Rasanen J, Huovinen J, Korhonen VE, Junkkari A, Kastinen S, Komulainen
S, et al. Diabetes is associated with familial idiopathic normal pressurehydrocephalus: a case–control comparison with family members. Fluids
Barriers CNS. 2020;17(1):57.
52. Ishikawa M, Yamada S, Yamamoto K. Three-dimensional observation of
Virchow-Robin spaces in the basal ganglia and white matter and theirrelevance to idiopathic normal pressure hydrocephalus. Fluids Barriers
CNS. 2015;12:15.

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Shigeki Yamada serve as board members of the International Society for Hydrocephalus and Cerebrospinal Fluid Disorders (ISHCSF).
Mitsuhito Mase serve as board members of the International Society for Hydrocephalus and Cerebrospinal Fluid Disorders (ISHCSF).
M.M. will be responsible for organizing the 16th Hydrocephalus
Meeting 2024 in Nagoya, Japan.